Autonomous Learning of Bipedal Walking Stabilization

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Humanoid robots, robots with a human-like body plan, enjoy increasing popularity as research platform, because they have the potential to be universal robots that can perform a large variety of tasks in environments that are designed to suit human needs.It is, however, a big challenge to replicate the efficiency, robustness, and grace of the natural human gait. Bipedal walkers are inherently unstable and difficult to control. In the first funding period of the Priority Programme Autonomous Learning, we developed a novel, closed-loop control approach for bipedal walking in the presence of large disturbances. Our capture step controller is able to absorb disturbances that can occur from any direction and at any time during a step and returns the robot to the desired walking velocity within one or two steps. To make the first capture step even more effective, we developed a method to learn in an online fashion a non-parametric model describing deviations from the point-mass model - from a small number of observations of residual energy after taking capture steps. For the second funding period of the SPP, we propose to investigate more advanced push recovery strategies that go beyond capture steps, i.e., using the rotational inertia of the torso to complement capture steps with balance-restoring torque. Furthermore, we propose the development of methods for online adaptation of steps to visually perceived constraints on foot placement. Again, parameters of our balance controllers will be learned online in a non-parametric way and the larger state-action space will be explored autonomously. These developments will extend the applicability of our balance control methods to restricted environments with constraints on foot placement, and also maintain balance in situations well beyond the limits of existing push recovery approaches.